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class="ident">get_embedding</span></span>(<span>z, dimred='umap', **kwargs)</span> +</code></dt> +<dd> +<div class="desc"><p>Get low-dimensional embeddings for visualizations.</p> +<h2 id="parameters">Parameters</h2> +<dl> +<dt><strong><code>z</code></strong> : <code>np.array</code></dt> +<dd><span><span class="MathJax_Preview">[N, d]</span><script type="math/tex">[N, d]</script></span> The latent variables.</dd> +<dt><strong><code>dimred</code></strong> : <code>str</code>, optional</dt> +<dd>'pca', 'tsne', or umap'.</dd> +<dt><strong><code>**kwargs</code></strong> : <code> </code></dt> +<dd>Extra key-value arguments for dimension reduction algorithms.</dd> +</dl> +<h2 id="returns">Returns:</h2> +<p>embed : np.array +<span><span class="MathJax_Preview">[N, 2]</span><script type="math/tex">[N, 2]</script></span> The latent variables after dimension reduction.</p></div> +</dd> +<dt id="VITAE.utils.get_igraph"><code class="name flex"> +<span>def <span class="ident">get_igraph</span></span>(<span>z, random_state=0)</span> +</code></dt> +<dd> +<div class="desc"><p>Get igraph for running Leidenalg clustering.</p> +<h2 id="parameters">Parameters</h2> +<dl> +<dt><strong><code>z</code></strong> : <code>np.array</code></dt> +<dd><span><span class="MathJax_Preview">[N, d]</span><script type="math/tex">[N, d]</script></span> The latent variables.</dd> +<dt><strong><code>random_state</code></strong> : <code>int</code>, optional</dt> +<dd>The random state.</dd> +</dl> +<h2 id="returns">Returns:</h2> +<p>g : igraph +The igraph object of connectivities.</p></div> +</dd> +<dt id="VITAE.utils.leidenalg_igraph"><code class="name flex"> +<span>def <span class="ident">leidenalg_igraph</span></span>(<span>g, res, random_state=0)</span> +</code></dt> +<dd> +<div class="desc"><p>Leidenalg clustering on an igraph object.</p> +<h2 id="parameters">Parameters</h2> +<dl> +<dt><strong><code>g</code></strong> : <code>igraph</code></dt> +<dd>The igraph object of connectivities.</dd> +<dt><strong><code>res</code></strong> : <code>float</code></dt> +<dd>The resolution parameter for Leidenalg clustering.</dd> +<dt><strong><code>random_state</code></strong> : <code>int</code>, optional</dt> +<dd>The random state.</dd> +</dl> +<h2 id="returns">Returns</h2> +<dl> +<dt><strong><code>labels</code></strong> : <code>np.array +</code></dt> +<dd><span><span class="MathJax_Preview">[N, ]</span><script type="math/tex">[N, ]</script></span> The clustered labels.</dd> +</dl></div> +</dd> +<dt id="VITAE.utils.plot_clusters"><code class="name flex"> +<span>def <span class="ident">plot_clusters</span></span>(<span>embed_z, labels, plot_labels=False, path=None)</span> +</code></dt> +<dd> +<div class="desc"><p>Plot the clustering results.</p> +<h2 id="parameters">Parameters</h2> +<dl> +<dt><strong><code>embed_z</code></strong> : <code>np.array</code></dt> +<dd><span><span class="MathJax_Preview">[N, 2]</span><script type="math/tex">[N, 2]</script></span> The latent variables after dimension reduction.</dd> +<dt><strong><code>labels</code></strong> : <code>np.array +</code></dt> +<dd><span><span class="MathJax_Preview">[N, ]</span><script type="math/tex">[N, ]</script></span> The clustered labels.</dd> +<dt><strong><code>plot_labels</code></strong> : <code>boolean</code>, optional</dt> +<dd>Whether to plot text of labels or not.</dd> +<dt><strong><code>path</code></strong> : <code>str</code>, optional</dt> +<dd>The path to save the figure.</dd> +</dl></div> +</dd> +<dt id="VITAE.utils.plot_marker_gene"><code class="name flex"> +<span>def <span class="ident">plot_marker_gene</span></span>(<span>expression, gene_name: str, embed_z, path=None)</span> +</code></dt> +<dd> +<div class="desc"><p>Plot the marker gene.</p> +<h2 id="parameters">Parameters</h2> +<dl> +<dt><strong><code>expression</code></strong> : <code>np.array</code></dt> +<dd><span><span class="MathJax_Preview">[N, ]</span><script type="math/tex">[N, ]</script></span> The expression of the marker gene.</dd> +<dt><strong><code>gene_name</code></strong> : <code>str</code></dt> +<dd>The name of the marker gene.</dd> +<dt><strong><code>embed_z</code></strong> : <code>np.array</code></dt> +<dd><span><span class="MathJax_Preview">[N, 2]</span><script type="math/tex">[N, 2]</script></span> The latent variables after dimension reduction.</dd> +<dt><strong><code>path</code></strong> : <code>str</code>, optional</dt> +<dd>The path to save the figure.</dd> +</dl></div> +</dd> +<dt id="VITAE.utils.plot_uncertainty"><code class="name flex"> +<span>def <span class="ident">plot_uncertainty</span></span>(<span>uncertainty, embed_z, path=None)</span> +</code></dt> +<dd> +<div class="desc"><p>Plot the uncertainty for all selected cells.</p> +<h2 id="parameters">Parameters</h2> +<dl> +<dt><strong><code>uncertainty</code></strong> : <code>np.array</code></dt> +<dd><span><span class="MathJax_Preview">[N, ]</span><script type="math/tex">[N, ]</script></span> The uncertainty of the all cells.</dd> +<dt><strong><code>embed_z</code></strong> : <code>np.array</code></dt> +<dd><span><span class="MathJax_Preview">[N, 2]</span><script type="math/tex">[N, 2]</script></span> The latent variables after dimension reduction.</dd> +<dt><strong><code>path</code></strong> : <code>str</code>, optional</dt> +<dd>The path to save the figure.</dd> +</dl></div> +</dd> +<dt id="VITAE.utils.DE_test"><code class="name flex"> +<span>def <span class="ident">DE_test</span></span>(<span>Y, X, gene_names, i_test, alpha: float = 0.05)</span> +</code></dt> +<dd> +<div class="desc"><p>Differential gene expression test.</p> +<h2 id="parameters">Parameters</h2> +<dl> +<dt><strong><code>Y</code></strong> : <code>numpy.array</code></dt> +<dd><span><span class="MathJax_Preview">n,</span><script type="math/tex">n,</script></span> the expression matrix.</dd> +<dt><strong><code>X</code></strong> : <code>numpy.array</code></dt> +<dd><span><span class="MathJax_Preview">n,1+1+s</span><script type="math/tex">n,1+1+s</script></span> the constant term, the pseudotime and the covariates.</dd> +<dt><strong><code>gene_names</code></strong> : <code>numpy.array</code></dt> +<dd><span><span class="MathJax_Preview">n,</span><script type="math/tex">n,</script></span> the names of all genes.</dd> +<dt><strong><code>i_test</code></strong> : <code>numpy.array</code></dt> +<dd>The indices of covariates to be tested.</dd> +<dt><strong><code>alpha</code></strong> : <code>float</code>, optional</dt> +<dd>The cutoff of p-values.</dd> +</dl> +<h2 id="returns">Returns</h2> +<dl> +<dt><strong><code>res_df</code></strong> : <code>pandas.DataFrame</code></dt> +<dd>The test results of expressed genes with two columns, +the estimated coefficients and the adjusted p-values.</dd> +</dl></div> +</dd> +<dt id="VITAE.utils.load_data"><code class="name flex"> +<span>def <span class="ident">load_data</span></span>(<span>path, file_name, return_dict=False)</span> +</code></dt> +<dd> +<div class="desc"><p>Load h5df data.</p> +<h2 id="parameters">Parameters</h2> +<dl> +<dt><strong><code>path</code></strong> : <code>str</code></dt> +<dd>The path of the h5 files.</dd> +<dt><strong><code>file_name</code></strong> : <code>str</code></dt> +<dd>The dataset name.</dd> +<dt><strong><code>return_dict</code></strong> : <code>boolean</code>, optional</dt> +<dd>Whether to return the dict of the dataset or not.</dd> +</dl> +<h2 id="returns">Returns:</h2> +<p>data : dict +The dict containing count, grouping, etc. of the dataset. +dd : anndata.AnnData +The AnnData object of the dataset.</p></div> +</dd> +<dt id="VITAE.utils.compute_kernel"><code class="name flex"> +<span>def <span class="ident">compute_kernel</span></span>(<span>x, y, kernel='rbf', **kwargs)</span> +</code></dt> +<dd> +<div class="desc"><p>Computes RBF kernel between x and y.</p> +<h2 id="parameters">Parameters</h2> +<pre><code>x: Tensor + Tensor with shape [batch_size, z_dim] +y: Tensor + Tensor with shape [batch_size, z_dim] +</code></pre> +<h2 id="returns">Returns</h2> +<pre><code>The computed RBF kernel between x and y +</code></pre></div> +</dd> +<dt id="VITAE.utils.squared_distance"><code class="name flex"> +<span>def <span class="ident">squared_distance</span></span>(<span>x, y)</span> +</code></dt> +<dd> +<div class="desc"><p>Compute the pairwise euclidean distance.</p> +<h2 id="parameters">Parameters</h2> +<dl> +<dt><strong><code>x</code></strong> : <code>Tensor</code></dt> +<dd>Tensor with shape [batch_size, z_dim]</dd> +<dt><strong><code>y</code></strong> : <code>Tensor</code></dt> +<dd>Tensor with shape [batch_size, z_dim]</dd> +</dl> +<h2 id="returns">Returns</h2> +<p>The pairwise euclidean distance between x and y.</p></div> +</dd> +<dt id="VITAE.utils.compute_mmd"><code class="name flex"> +<span>def <span class="ident">compute_mmd</span></span>(<span>x, y, kernel, **kwargs)</span> +</code></dt> +<dd> +<div class="desc"><p>Computes Maximum Mean Discrepancy(MMD) between x and y.</p> +<h2 id="parameters">Parameters</h2> +<dl> +<dt><strong><code>x</code></strong> : <code>Tensor</code></dt> +<dd>Tensor with shape [batch_size, z_dim]</dd> +<dt><strong><code>y</code></strong> : <code>Tensor</code></dt> +<dd>Tensor with shape [batch_size, z_dim]</dd> +<dt><strong><code>kernel</code></strong> : <code>str</code></dt> +<dd>The kernel type used in MMD. It can be 'rbf', 'multi-scale-rbf' or 'raphy'.</dd> +<dt><strong><code>**kwargs</code></strong> : <code>dict</code></dt> +<dd>The parameters used in kernel function.</dd> +</dl> +<h2 id="returns">Returns</h2> +<dl> +<dt><code>The computed MMD between x and y</code></dt> +<dd> </dd> +</dl></div> +</dd> +<dt id="VITAE.utils.sample_z"><code class="name flex"> +<span>def <span class="ident">sample_z</span></span>(<span>args)</span> +</code></dt> +<dd> +<div class="desc"><p>Samples from standard Normal distribution with shape [size, z_dim] and +applies re-parametrization trick. It is actually sampling from latent +space distributions with N(mu, var) computed in <code>_encoder</code> function.</p> +<h2 id="parameters">Parameters</h2> +<dl> +<dt><strong><code>args</code></strong> : <code>list</code></dt> +<dd>List of [mu, log_var] computed in <code>_encoder</code> function.</dd> +</dl> +<h2 id="returns">Returns</h2> +<p>The computed Tensor of samples with shape [size, z_dim].</p></div> +</dd> +</dl> +</section> +<section> +<h2 class="section-title" id="header-classes">Classes</h2> +<dl> +<dt id="VITAE.utils.Early_Stopping"><code class="flex name class"> +<span>class <span class="ident">Early_Stopping</span></span> +<span>(</span><span>warmup=0, patience=10, tolerance=0.001, relative=False, is_minimize=True)</span> +</code></dt> +<dd> +<div class="desc"><p>The early-stopping monitor.</p></div> +<details class="source"> +<summary> +<span>Expand source code</span> +</summary> +<pre><code class="python">class Early_Stopping(): + ''' + The early-stopping monitor. + ''' + def __init__(self, warmup=0, patience=10, tolerance=1e-3, + relative=False, is_minimize=True): + self.warmup = warmup + self.patience = patience + self.tolerance = tolerance + self.is_minimize = is_minimize + self.relative = relative + + self.step = -1 + self.best_step = -1 + self.best_metric = np.inf + + if not self.is_minimize: + self.factor = -1.0 + else: + self.factor = 1.0 + + def __call__(self, metric): + self.step += 1 + + if self.step < self.warmup: + return False + elif (self.best_metric==np.inf) or \ + (self.relative and (self.best_metric-metric)/self.best_metric > self.tolerance) or \ + ((not self.relative) and self.factor*metric<self.factor*self.best_metric-self.tolerance): + self.best_metric = metric + self.best_step = self.step + return False + elif self.step - self.best_step>self.patience: + print('Best Epoch: %d. Best Metric: %f.'%(self.best_step, self.best_metric)) + return True + else: + return False</code></pre> +</details> +</dd> +</dl> +</section> +</article> +<nav id="sidebar"> +<div class="toc"> +<ul></ul> +</div> +<ul id="index"> +<li><h3>Super-module</h3> +<ul> +<li><code><a title="VITAE" href="index.html">VITAE</a></code></li> +</ul> +</li> +<li><h3><a href="#header-functions">Functions</a></h3> +<ul class="two-column"> +<li><code><a title="VITAE.utils.reset_random_seeds" href="#VITAE.utils.reset_random_seeds">reset_random_seeds</a></code></li> +<li><code><a title="VITAE.utils.get_embedding" href="#VITAE.utils.get_embedding">get_embedding</a></code></li> +<li><code><a title="VITAE.utils.get_igraph" href="#VITAE.utils.get_igraph">get_igraph</a></code></li> +<li><code><a title="VITAE.utils.leidenalg_igraph" href="#VITAE.utils.leidenalg_igraph">leidenalg_igraph</a></code></li> +<li><code><a title="VITAE.utils.plot_clusters" href="#VITAE.utils.plot_clusters">plot_clusters</a></code></li> +<li><code><a title="VITAE.utils.plot_marker_gene" href="#VITAE.utils.plot_marker_gene">plot_marker_gene</a></code></li> +<li><code><a title="VITAE.utils.plot_uncertainty" href="#VITAE.utils.plot_uncertainty">plot_uncertainty</a></code></li> +<li><code><a title="VITAE.utils.DE_test" href="#VITAE.utils.DE_test">DE_test</a></code></li> +<li><code><a title="VITAE.utils.load_data" href="#VITAE.utils.load_data">load_data</a></code></li> +<li><code><a title="VITAE.utils.compute_kernel" href="#VITAE.utils.compute_kernel">compute_kernel</a></code></li> +<li><code><a title="VITAE.utils.squared_distance" href="#VITAE.utils.squared_distance">squared_distance</a></code></li> +<li><code><a title="VITAE.utils.compute_mmd" href="#VITAE.utils.compute_mmd">compute_mmd</a></code></li> +<li><code><a title="VITAE.utils.sample_z" href="#VITAE.utils.sample_z">sample_z</a></code></li> +</ul> +</li> +<li><h3><a href="#header-classes">Classes</a></h3> +<ul> +<li> +<h4><code><a title="VITAE.utils.Early_Stopping" href="#VITAE.utils.Early_Stopping">Early_Stopping</a></code></h4> +</li> +</ul> +</li> +</ul> +</nav> +</main> +<footer id="footer"> +<p>Generated by <a href="https://pdoc3.github.io/pdoc" title="pdoc: Python API documentation generator"><cite>pdoc</cite> 0.11.1</a>.</p> +</footer> +</body> +</html>